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ralphinho-rfc-pipeline

Decompose large software features into dependency-tracked work units with quality gates.

Updated Mar 26, 2026
Search Tags:#quality gates#feature decomposition#multi-agent workflow#merge queue#dag orchestration#rfc pipeline#work unit decomposition
One-click install
npx skills add https://github.com/luongldptit/move-ticket --skill ralphinho-rfc-pipeline-luongldptit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/luongldptit/move-ticket/tree/main/.agent/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/luongldptit/move-ticket --skill ralphinho-rfc-pipeline-luongldptit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Large, complex software features often exceed the scope of single-agent workflows, leading to incomplete work, integration failures, and unverifiable outputs when executed without structured decomposition.

Core Features & Use Cases

  • RFC-Driven DAG Decomposition: Break large feature specifications into independent, dependency-tracked work units for parallel execution.
  • Quality Gates & Merge Queues: Enforce validation checks for each work unit before integration, preventing broken code from entering the main branch.
  • Use Case: When developing a new customer-facing analytics dashboard with interconnected components (data ingestion, visualization, permissions, export), use this Skill to split work into isolated testable units, validate each, and merge safely via a queue to avoid disrupting existing functionality.

Quick Start

Use the ralphinho-rfc-pipeline skill to decompose the new mobile app push notification feature into independently verifiable work units with dependency tracking and pre-merge quality checks.

Frequently Asked Questions about ralphinho-rfc-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I decompose large software features into manageable work units for parallel execution?▼

Large software features can be decomposed into manageable work units by using an RFC-driven DAG pipeline. This approach breaks complex specifications into independent, dependency-tracked tasks that can be executed and verified in parallel.

What is the best way to enforce quality gates and manage a merge queue for multi-component features?▼

The best way to enforce quality gates and manage a merge queue is through RFC-driven DAG orchestration. This method validates each isolated work unit before integration, preventing broken code from entering the main branch during multi-component feature development.

When do I need DAG-based task orchestration for software engineering projects?▼

You need DAG-based task orchestration when executing large, complex software features that exceed single-agent workflow scope. It is essential for scenarios involving multi-component development, cross-team collaboration, and regulated code changes requiring strict validation.

Can I use RFC-driven pipeline orchestration for cross-team collaboration and regulated code changes?▼

Yes, RFC-driven pipeline orchestration supports cross-team collaboration and regulated code changes by splitting work into isolated testable units. It enforces strict validation and audit trails for each dependency-tracked work unit before merging.

How does work unit decomposition handle failure recovery for stalled tasks in a feature pipeline?▼

Work unit decomposition handles failure recovery by isolating stalled tasks within a dependency-tracked DAG. This allows the pipeline to target and recover specific failed work units independently without disrupting the entire feature execution.

Does ralphinho-rfc-pipeline require specific dependencies for executing complex software features?▼

No, ralphinho-rfc-pipeline operates without external dependencies to execute complex software features. It internally manages DAG orchestration, quality gate enforcement, and merge queue management for large-scale feature decomposition.

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